Abstract

Cancer remains a significant global health concern, and the development of effective anticancer medications is crucial for improving patient outcomes. However, predicting the efficacy of different anticancer drugs is a complex task due to the heterogeneity of cancer and the multifactorial nature of drug response. In this study, we propose a novel approach that combines multi-target regression and support vector regression analysis to accurately predict the efficacy of various anticancer medications. The selection of appropriate anticancer medications for individual patients is a challenging task. While several predictive models have been developed, most of them focus on single drugs and fail to capture the intricate relationships between multiple drugs and their targets. To overcome these limitations, we present a comprehensive framework that utilizes multi-target regression and support vector regression analysis for precise prediction of anticancer medication efficacy. To further enhance the predictive performance, we integrate support vector regression analysis into our framework. Support vector regression is a powerful machine learning algorithm that can effectively handle high-dimensional datasets and nonlinear relationships. It allows us to build robust models that can accurately predict anticancer medication efficacy.

Keywords: Exact prediction; Anti-cancer medication efficacy; Multitarget regression; Support vector regression analysis